OpenRaiser/Intern-Atlas huggingface dataset:
Studying Product Competition Using Representation Learning
Venue: arXiv cs.LG 2020
Year: 2020

Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. However, it is computationally challenging to analyze product-level competition for the millions of products available on e-commerce platforms. We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec, to study product-level competition, when the number of products is large. The proposed model takes shopping baskets as inputs and, for every product, generates a low-dimensional embedding that preserves important product information. In order for the product embeddings to be useful for firm strategic decision making, we leverage economic theories and causal inference to propose two modifications to Word2Vec. First of all, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Second, we combine these vectors with random utility-based choice models to forecast demand. To accurately estimate price elasticities, i.e., how demand responds to changes in price, we modify Word2Vec by removing the influence of price from the product vectors. We show that, compared with state-of-the-art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.
Robotics Meets Cosmetic Dermatology: Development of a Novel Vision-Guided System for Skin Photo-Rejuvenation
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we present a novel robotic system for skin photo-rejuvenation procedures, which can uniformly deliver the laser's energy over the skin of the face. The robotised procedure is performed by a manipulator whose end-effector is instrumented with a depth sensor, a thermal camera, and a cosmetic laser generator. To plan the heat stimulating trajectories for the laser, the system computes the surface model of the face and segments it into seven regions that are automatically filled with laser shots. We report experimental results with human subjects to validate the performance of the system. To the best of the author's knowledge, this is the first time that facial skin rejuvenation has been automated by robot manipulators.
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
Venue: arXiv cs.IR 2020
Year: 2020

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting). This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning. Our key intuition is to guide neural collaborative filtering with domain-invariant components shared across the dense and sparse domains, improving the user and item representations learned in the sparse domains. We leverage contextual invariances across domains to develop these shared modules, and demonstrate that with user-item interaction context, we can learn-to-learn informative representation spaces even with sparse interaction data. We show the effectiveness and scalability of our approach on two public datasets and a massive transaction dataset from Visa, a global payments technology company (19% Item Recall, 3x faster vs. training separate models for each domain). Our approach is applicable to both implicit and explicit feedback settings.
Panoptic Instance Segmentation on Pigs
Venue: arXiv cs.CV 2020
Year: 2020

The behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Especially systems based on computer vision have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown pleasingly good results. Especially object and keypoint detectors have been used to detect the individual animals. Despite good results, bounding boxes and sparse keypoints do not trace the contours of the animals, resulting in a lot of information being lost. Therefore this work follows the relatively new definition of a panoptic segmentation and aims at the pixel accurate segmentation of the individual pigs. For this a framework of a neural network for semantic segmentation, different network heads and postprocessing methods is presented. With the resulting instance segmentation masks further information like the size or weight of the animals could be estimated. The method is tested on a specially created data set with 1000 hand-labeled images and achieves detection rates of around 95% (F1 Score) despite disturbances such as occlusions and dirty lenses.
On Characterization of Entropic Vectors at the Boundary of Almost Entropic Cones
Venue: arXiv cs.IT 2020
Year: 2020

The entropy region is a fundamental object in information theory. An outer bound for the entropy region is defined by a minimal set of Shannon-type inequalities called elemental inequalities also referred to as the Shannon region. This paper focuses on characterization of the entropic points at the boundary of the Shannon region for three random variables. The proper faces of the Shannon region form its boundary. We give new outer bounds for the entropy region in certain faces and show by explicit construction of distributions that the existing inner bounds for the entropy region in certain faces are not tight.
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
Venue: arXiv cs.IR 2020
Year: 2020

In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interactions with items in one domain but not in the other domain. To the latter, these users are considered as cold-start users. How to effectively transfer users' preferences based on their interactions from one domain to the other relevant domain, is the key issue in cross-domain recommendation. Inspired by the advances made in review-based recommendation, we propose to model user preference transfer at aspect-level derived from reviews. To this end, we propose a cross-domain recommendation framework via aspect transfer network for cold-start users (named CATN). CATN is devised to extract multiple aspects for each user and each item from their review documents, and learn aspect correlations across domains with an attention mechanism. In addition, we further exploit auxiliary reviews from like-minded users to enhance a user's aspect representations. Then, an end-to-end optimization framework is utilized to strengthen the robustness of our model. On real-world datasets, the proposed CATN outperforms SOTA models significantly in terms of rating prediction accuracy. Further analysis shows that our model is able to reveal user aspect connections across domains at a fine level of granularity, making the recommendation explainable.
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
Venue: arXiv cs.CV 2020
Year: 2020

The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision. This requirement is hard to fulfil because most of the training sets available to develop these systems only contain global annotations, making the localisation of diseases a weakly supervised approach. The main methods designed for weakly supervised disease classification and localisation rely on saliency or attention maps that are not specifically trained for localisation, or on region proposals that can not be refined to produce accurate detections. In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation. Using the ChestX-ray14 data set, we show that our proposed model establishes the new state-of-the-art for weakly-supervised disease diagnosis and localisation.
A Recommender System For Open Educational Videos Based On Skill Requirements
Venue: arXiv cs.CY 2020
Year: 2020

In this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.
CovidChain: An Anonymity Preserving Blockchain Based Framework for Protection Against Covid-19
Venue: arXiv cs.CR 2020
Year: 2020

Today, the entire world is facing incredible health and economic challenges due to the rapid spread of the life threatening novel Coronavirus Disease - 2019 (COVID-19). In the prevailing situation when a vaccine is many months away, the way forward seems to be a controlled exit from the lockdown - where, infected/exposed people are strictly quarantined and recovered/unexposed people are allowed to carry on with their day to day business activities. However, appropriate physical distancing norms will have to be strictly followed for such relaxations. Therefore, mechanisms are required that will assist people in following the social and physical distancing norms in public places. In this paper, we propose an anonymity preserving blockchain based framework that allows people, through use of their smart phones and other communication devices, to protect themselves from infections as they conduct their daily business activities.
Combinatorial two-stage minmax regret problems under interval uncertainty
Venue: arXiv cs.DS 2020
Year: 2020

In this paper a class of combinatorial optimization problems is discussed. It is assumed that a feasible solution can be constructed in two stages. In the first stage the objective function costs are known while in the second stage they are uncertain and belong to an interval uncertainty set. In order to choose a solution, the minmax regret criterion is used. Some general properties of the problem are established and results for two particular problems, namely the shortest path and the selection problem, are shown.
Near Instance-Optimality in Differential Privacy
Venue: arXiv cs.CR 2020
Year: 2020

We develop two notions of instance optimality in differential privacy, inspired by classical statistical theory: one by defining a local minimax risk and the other by considering unbiased mechanisms and analogizing the Cramer-Rao bound, and we show that the local modulus of continuity of the estimand of interest completely determines these quantities. We also develop a complementary collection mechanisms, which we term the inverse sensitivity mechanisms, which are instance optimal (or nearly instance optimal) for a large class of estimands. Moreover, these mechanisms uniformly outperform the smooth sensitivity framework on each instance for several function classes of interest, including real-valued continuous functions. We carefully present two instantiations of the mechanisms for median and robust regression estimation with corresponding experiments.
The CLEARSY Safety Platform: 5 Years of Research, Development and Deployment
Venue: arXiv cs.SE 2020
Year: 2020

The CLEARSY Safety Platform (CSSP) was designed to ease the development of safety critical systems and to reduce the overall costs (development, deployment, and certification) under the pressure of the worldwide market. A smart combination of hardware features (double processor) and formal method (B method and code generators) was used to produce a SIL4-ready platform where safety principles are built-in and cannot be altered by the developer. Summarizing a 5-year return of experience in the effective application in the railways, this article explains how this approach is a game-changer and tries to anticipate the future of this platform for safety critical systems. In particular, the education of future engineers and the seamless integration in existing engineering processes with the support of Domain Specific Languages are key topics for a successful deployment in other domains. DSL like Robosim to program mobile robots and relay circuits to design railway signalling systems are connected to the platform.
CSNE: Conditional Signed Network Embedding
Venue: arXiv cs.SI 2020
Year: 2020

Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing embedding methods for sign prediction generally enforce different notions of status or balance theories in their optimization function. These theories, however, are often inaccurate or incomplete, which negatively impacts method performance. In this context, we introduce conditional signed network embedding (CSNE). Our probabilistic approach models structural information about the signs in the network separately from fine-grained detail. Structural information is represented in the form of a prior, while the embedding itself is used for capturing fine-grained information. These components are then integrated in a rigorous manner. CSNE's accuracy depends on the existence of sufficiently powerful structural priors for modelling signed networks, currently unavailable in the literature. Thus, as a second main contribution, which we find to be highly valuable in its own right, we also introduce a novel approach to construct priors based on the Maximum Entropy (MaxEnt) principle. These priors can model the \emph{polarity} of nodes (degree to which their links are positive) as well as signed \emph{triangle counts} (a measure of the degree structural balance holds to in a network). Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt priors on their own, while less accurate than full CSNE, achieve accuracies competitive with the state-of-the-art at very limited computational cost, thus providing an excellent runtime-accuracy trade-off in resource-constrained situations.
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation
Venue: arXiv cs.CL 2020
Year: 2020

Open Domain dialog system evaluation is one of the most important challenges in dialog research. Existing automatic evaluation metrics, such as BLEU are mostly reference-based. They calculate the difference between the generated response and a limited number of available references. Likert-score based self-reported user rating is widely adopted by social conversational systems, such as Amazon Alexa Prize chatbots. However, self-reported user rating suffers from bias and variance among different users. To alleviate this problem, we formulate dialog evaluation as a comparison task. We also propose an automatic evaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) that automatically cleans self-reported user ratings as it trains on them. Specifically, we first use a self-supervised method to learn better dialog feature representation, and then use KNN and Shapley to remove confusing samples. Our experiments show that CMADE achieves 89.2% accuracy in the dialog comparison task.
HQC-RMRS, an instantiation of the HQC encryption framework with a more efficient auxiliary error-correcting code
Venue: arXiv cs.CR 2020
Year: 2020

The HQC encryption framework is a general code-based encryption scheme for which decryption returns a noisy version of the plaintext. Any instantiation of the scheme will therefore use an error-correcting procedure relying on a fixed auxiliary code. Unlike the McEliece encryption framework whose security is directly related to how well one can hide the structure of an error-correcting code, the security reduction of the HQC encryption framework is independent of the nature of the auxiliary decoding procedure which is publicly available. What is expected from it is that the decoding algorithm is both efficient and has a decoding failure rate which can be easily modelized and analyzed. The original error-correction procedure proposed for the HQC framework was to use tensor products of BCH codes and repetition codes. In this paper we consider another code family for removing the error vector deriving from the general framework: the concatenation of Reed-Muller and Reed-Solomon codes. We denote this instantiation of the HQC framework by HQC-RMRS. These codes yield better decoding results than the BCH and repetition codes: overall we gain roughly 17\% in the size of the key and the ciphertext, while keeping a simple modelization of the decoding error rate. The paper also presents a simplified and more precise analysis of the distribution of the error vector output by the HQC protocol.
Authentication and Key Management Automation in Decentralized Secure Email and Messaging via Low-Entropy Secrets
Venue: arXiv cs.CR 2020
Year: 2020

We revisit the problem of entity authentication in decentralized end-to-end encrypted email and secure messaging to propose a practical and self-sustaining cryptographic solution based on password-authenticated key exchange (PAKE). This not only allows users to authenticate each other via shared low-entropy secrets, e.g., memorable words, without a public key infrastructure or a trusted third party, but it also paves the way for automation and a series of cryptographic enhancements; improves security by minimizing the impact of human error and potentially improves usability. First, we study a few vulnerabilities in voice-based out-of-band authentication, in particular a combinatorial attack against lazy users, which we analyze in the context of a secure email solution. Next, we propose solving the problem of secure equality test using PAKE to achieve entity authentication and to establish a shared high-entropy secret key. Our solution lends itself to offline settings, compatible with the inherently asynchronous nature of email and modern messaging systems. The suggested approach enables enhancements in key management such as automated key renewal and future key pair authentications, multi-device synchronization, secure secret storage and retrieval, and the possibility of post-quantum security as well as facilitating forward secrecy and deniability in a primarily symmetric-key setting. We also discuss the use of auditable PAKEs for mitigating a class of online guess and abort attacks in authentication protocols.
A note on the properties of associated Boolean functions of quadratic APN functions
Venue: arXiv cs.DM 2020
Year: 2020

Let $F$ be a quadratic APN function of $n$ variables. The associated Boolean function $γ_F$ in $2n$ variables ($γ_F(a,b)=1$ if $a\neq{\bf 0}$ and equation $F(x)+F(x+a)=b$ has solutions) has the form $γ_F(a,b) = Φ_F(a) \cdot b + φ_F(a) + 1$ for appropriate functions $Φ_F:\mathbb{F}_2^n\to \mathbb{F}_2^n$ and $φ_F:\mathbb{F}_2^n\to \mathbb{F}_2$. We summarize the known results and prove new ones regarding properties of $Φ_F$ and $φ_F$. For instance, we prove that degree of $Φ_F$ is either $n$ or less or equal to $n-2$. Based on computation experiments, we formulate a conjecture that degree of any component function of $Φ_F$ is $n-2$. We show that this conjecture is based on two other conjectures of independent interest.
On the Locality of the Natural Gradient for Deep Learning
Venue: arXiv cs.LG 2020
Year: 2020

We study the natural gradient method for learning in deep Bayesian networks, including neural networks. There are two natural geometries associated with such learning systems consisting of visible and hidden units. One geometry is related to the full system, the other one to the visible sub-system. These two geometries imply different natural gradients. In a first step, we demonstrate a great simplification of the natural gradient with respect to the first geometry, due to locality properties of the Fisher information matrix. This simplification does not directly translate to a corresponding simplification with respect to the second geometry. We develop the theory for studying the relation between the two versions of the natural gradient and outline a method for the simplification of the natural gradient with respect to the second geometry based on the first one. This method suggests to incorporate a recognition model as an auxiliary model for the efficient application of the natural gradient method in deep networks.
New Approximation Algorithms for Maximum Asymmetric Traveling Salesman and Shortest Superstring
Venue: arXiv cs.DS 2020
Year: 2020

In the maximum asymmetric traveling salesman problem (Max ATSP) we are given a complete directed graph with nonnegative weights on the edges and we wish to compute a traveling salesman tour of maximum weight. In this paper we give a fast combinatorial $\frac{7}{10}$-approximation algorithm for Max ATSP. It is based on techniques of {\em eliminating} and {\em diluting} problematic subgraphs with the aid of {\it half-edges} and a method of edge coloring. (A {\it half-edge} of edge $(u,v)$ is informally speaking "either a head or a tail of $(u,v)$".) A novel technique of {\em diluting} a problematic subgraph $S$ consists in a seeming reduction of its weight, which allows its better handling. The current best approximation algorithms for Max ATSP, achieving the approximation guarantee of $\frac 23$, are due to Kaplan, Lewenstein, Shafrir, Sviridenko (2003) and Elbassioni, Paluch, van Zuylen (2012). Using a result by Mucha, which states that an $α$-approximation algorithm for Max ATSP implies a $(2+\frac{11(1-α)}{9-2α})$-approximation algorithm for the shortest superstring problem (SSP), we obtain also a $(2 \frac{33}{76} \approx 2,434)$-approximation algorithm for SSP, beating the previously best known (having an approximation factor equal to $2 \frac{11}{23} \approx 2,4782$.)